"A database that can be operated or managed" matching MCP servers:

Enables AI assistants to interact with self-hosted Dynatrace Managed environments to retrieve observability data, security insights, and performance metrics. It allows users to query problems, logs, events, and SLOs through natural language interfaces in both local and remote modes.

MCP server for multiple databases (PostgreSQL, MySQL, MariaDB, SQLite, MongoDB, Redis) with tools for schema inspection, querying, performance diagnostics, and safe write operations, featuring access modes, PII masking, and audit logging.

An MCP server that analyzes SQL schemas, generates realistic test data, benchmarks queries, and provides optimization recommendations to improve database performance.

Enables Managed Agent sessions to retrieve provider-specific alarms, metrics, and logs through MCP tools, supporting mock and AWS-backed monitoring data.

An MCP server that provides cost and reliability observability for LLM and agent workflows. It records model calls and allows querying and aggregating telemetry data through MCP tools.

Enables AI agents to monitor Docker containers, system resources, ports, and logs in real-time.

An MCP server for Grafana Loki that enables LLMs to query, tail, and analyze logs, featuring fuzzy container-name matching, compact output, and tools for log patterns, volume, and label discovery. It can run as a local stdio server or a containerized HTTP service.

Enables querying OpenObserve logs—listing streams, inspecting schemas, and running SearchSQL time-windowed searches—so you can answer what actually happened in production. Optionally correlates those logs against a database through a verified read-only connection via DbSchema and DbQuery tools.

Exposes Rancher-managed clusters as tools for AI assistants, enabling SRE tasks like inspecting clusters, workloads, pods, logs, and events.

Streams live progress from long-running MCP tool calls to any connected client. Instrument a tool call with mcp-telemetry-sdk, and telemetry_subscribe pushes its steps, logs, cost, and completion over notifications/progress — no polling, and a job started in one session can be watched from a different one.

Gates every AI agent tool call against your policy before it runs, then writes a hash-chained, tamper-evident record of what was decided and what happened. Anyone can verify that record offline, without your software, your systems or any key you hold.

Enables agents to triage Windows EVTX event logs, hunt with Sigma and Chainsaw rules, detect log tampering and timeline gaps, and author or lint detection rules. Results are returned as server-minted handles with paged summaries so tens of thousands of detections can be worked through without pulling raw logs into context.

A Model Context Protocol (MCP) server for PostgreSQL that provides safe, structured access to your database for AI assistants, enabling health checks, index tuning, lock analysis, and more.

Checks what local coding agents such as Claude Code, Codex and Cursor really did: files changed, commands run and test results read from test output, not the agent's word. Unclear test results can be double-checked by Laya (on your computer) or TypeSafe's Jev (cloud, your own API key). Read-only.

Lets an assistant read recorded production AI agent runs — steps, model and tool calls with arguments, and outcomes — along with the findings of comparison runs that checked a prompt or model change against those recordings, and verify a signed evidence bundle offline with no account or network. It is strictly read-only: no tool starts a replay or comparison, so nothing can spend money or act without a person deciding.

Enables Azure SQL Database performance and administration through typed execution, query benchmarking, and managed DBA operations.

Enables log analysis, searching, counting, and system metrics retrieval (CPU, memory, disk) via natural language, using 5 tools that can be integrated with Claude Code.

A Claude Code MCP server that connects to Laravel Clockwork to provide insights into application performance, database queries, errors, and request flow.

A portable, read-only Model Context Protocol server for turning observability data into bounded evidence that AI agents can inspect safely.

Enables AI agents to connect to tools and data through a governed MCP endpoint with agent identity, scoped permissions, guardrails, and full audit trails. Access is user-controlled and can be revoked instantly, with every tool call logged by agent identity and timestamp.

Enables AI agents and assistants to manage a SutramX workspace by listing and changing monitors, reading check results, handling incidents, and managing status pages through the SutramX API. Access can be limited to read-only or optionally allow destructive operations.

A flight recorder for Claude Code that captures tool calls and observations into a SQLite database, then classifies them into a distilled timeline for incident troubleshooting.

Enables an MCP-capable agent to operate a heterogeneous cluster of Debian/Armbian Linux nodes over SSH from a single inventory, inspecting status and logs, managing packages, services, files, storage and NFS, and deploying new capabilities to nodes through installable modules. Built-in cluster tools fan out to nodes by explicit names, roles or tags with bounded concurrency, while modules can be installed on compatible nodes and run on demand or as systemd services.

Enables running Splunk searches and reading their results against a Splunk Cloud deployment that requires interactive Microsoft Entra SSO and MFA, using a Playwright-managed authenticated browser session instead of API tokens.

Monitors MCP protocol traffic across legacy and modern lanes, records usage evidence in a durable SQLite ledger, and recommends when the legacy fallback can safely be removed.

Read-only MCP server for TrueNAS Scale debugging, providing k3s tools for kubectl operations and whitelisted midclt calls for system info, apps, and pools.

Exposes SereneDB server state as read-only MCP tools (status, storage, memory, activity, threads, profile, callgraph, host, config, and optional set_setting) so agents can query live database health and metrics with evidence-backed findings instead of viewing screenshots.

Connects AI assistants to a self-hosted workflow layer so they can create jobs, browse available work, claim tasks, update status, retrieve job details, share structured context, and record review or approval events through exposed tools and prompts.

Enables AI agents to reach upstream MCP tool servers through a central policy gateway that authenticates each agent, enforces least-privilege and default-deny rules, and applies response obligations such as redaction, inference stripping, PII masking, and truncation. It also records every decision in a tamper-evident, hash-chained audit log that can be verified later.

Exposes knowledge retrieval to AI tools over MCP with dense, sparse, and hybrid search, RRF fusion, configurable reranking, and modular ingestion of PDF/Markdown documents including image captioning. It also provides query tracing and Ragas-based evaluation so pipelines can be swapped, debugged, and tested independently.